| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,230 |
| تعداد مقالات | 35,776 |
| تعداد مشاهده مقاله | 54,004,666 |
| تعداد دریافت فایل اصل مقاله | 93,164,890 |
مقایسه عملکرد مدلهای اولیه و بهینهشده اتوماسیون سلولی در پیشبینی گسترش آتشسوزی جنگل | ||
| تحقیقات جنگل و صنوبر ایران | ||
| دوره 28، شماره 4 - شماره پیاپی 82، اسفند 1399، صفحه 351-369 اصل مقاله (1.62 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2020.351429.1945 | ||
| نویسندگان | ||
| محمد واثق الحاج خلف1؛ شعبان شتایی* 2؛ رقیه جهدی3 | ||
| 1دانشجوی کارشناسی ارشد جنگلداری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 2استاد، گروه جنگلداری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 3استادیار، گروه علوم و مهندسی جنگل، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| چکیده | ||
| مدل اتوماسیون سلولی بهعنوان یکی از پرکاربردترین مدلهای شبیهسازی گسترش آتش، نیازمند بهکارگیری پارامترهای مناسب و ضریبهای دقیق هستند. هدف اصلی پژوهش پیشرو، مقایسه مدلهای اولیه اتوماسیون سلولی و مدل بهینهشده با استفاده از الگوریتم کلونی زنبورعسل بهمنظور پیشبینی نرخ گسترش آتش در یک جنگل دستکاشت در شمال شرقی استان گلستان است. با تهیه نقشه واقعیت زمینی نمونهای، قابلیت دادههای طیفی نوری و راداری ماهوارههای سنتینل 1 و 2 برای تهیه نقشه تیپ و تراکم پوشش گیاهی مورد نیاز در مدلسازی گسترش آتش بهعنوان هدف فرعی بررسی شد. پس از تصحیح تصاویر و استخراج شاخصهای گیاهی، نقشههای نوع و تراکم پوشش گیاهی با استفاده از الگوریتم جنگل تصادفی تهیه شد. نتایج ارزیابی صحت نشان داد که بهترین نتیجه با تلفیق دادههای نوری و راداری بهدست میآید (صحت کلی و ضریب کاپا بهترتیب 81/0 و 75/0). مدلسازی گسترش آتش با استفاده از مدلهای اولیه و نیز با ضریبهای بهینهشده در پژوهشهای پیشین انجام شد. بهمنظور بهبود نتایج و بررسی مقایسهای، ضریبهای مدل براساس شرایط منطقه با استفاده از الگوریتم کلونی زنبورعسل بهینهسازی شد. سپس، مدلسازی تکرار شد و با آتشسوزی واقعی مقایسه شد. نتایج نشان داد که مدلهای بهینهشده با الگوریتم کلونی زنبورعسل (بهینهشده Alexandridis و همکاران (2011) با صحت کلی، ضریب کاپا و ضریب سورنسن بهترتیب 92/0، 74/0 و 78/0 و بهینهشده Alexandridis و همکاران (2008) بهترتیب 93/0، 74/0 و 78/0) مطابقت بیشتری با آتشسوزی واقعی در مقایسه با مدلهای اولیه و بهینهشده پیشین داشتند. | ||
| کلیدواژهها | ||
| الگوریتم کلونی زنبورعسل؛ بهینهسازی؛ جنگل دستکاشت؛ سنتینل | ||
| عنوان مقاله [English] | ||
| Comparative study of basic and Bee Colony-optimized models cellular automation for prediction of wildfire spread | ||
| نویسندگان [English] | ||
| M.W. Alhaj khalaf1؛ Sh. Shataee2؛ R. Jahdi3 | ||
| 1M.Sc. Student of Forestry, Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2Prof., Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Assistant Prof., Department of Forest Sciences and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده [English] | ||
| As one of the most widely used models for fire spread simulation, cellular automata models commonly require appropriate parameters and the optimized coefficients. The main aim of this study was to compare the basic models of cell automation set by previous studies and optimized models using the Bee Colony Algorithm (ABC) to predict the rate of fire spread in a reforestation area in northeastern Golestan province, Iran. Furthermore, a sub-objective was to test the ability of optical and radar sensors (Sentinel 1 and 2) for mapping the vegetation stand type and density required for fire spread modeling. Following pre-processing and extraction of vegetation indices, vegetation type and density were mapped by the Random Forest algorithm. The accuracy assessment showed that the best result was obtained by combining the optical and radar data (Kappa coefficient (KC) = 0.75 and Overall accuracy (OA) = 0.81). Moreover, the fire spread was modeled using previous and optimized coefficients from previous studies. Model coefficients were optimized based on environmental conditions using the BCA algorithm and were compared with the occurred fire to improve the results and comparative evaluation. The results showed that the optimized models were more consistent (Sorenson coefficient (SC) = 0.78; KC = 0.74 and OA = 0.93 for Alexandridis et al., 2011; and SC = 0.78; KC = 0.74 and OA = 0.92 for Alexandridis et al., 2008) with the observed fire than the other applied cellular automation models. | ||
| کلیدواژهها [English] | ||
| Bee colony algorithm, Golestan Province, optimization, reforestation, Sentinel | ||
| مراجع | ||
|
- Alexandridis, A., Russo, L., Vakalis, D., Bafas, G.V. and Siettos, C.I., 2011. Wildland fire spread modelling using cellular automata: Evolution in large-scale spatially heterogeneous environments under fire suppression tactics. International Journal of Wildland Fire, 20(5): 633-647. - Alexandridis, A., Vakalis, D., Siettos, C.I. and Bafas, G.V., 2008. A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990. Applied Mathematics and Computation, 204(1): 191-201. - Andela, N., Morton, D.C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G.R. and Randerson, J.T., 2019. The global fire atlas of individual fire size, duration, speed and direction. Earth System Science Data, 11(2): 529-552. - Baumann, M., Levers, C., Macchi, L., Bluhm, H., Waske, B., Gasparri, N.I. and Kuemmerle, T., 2018. Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data. Remote Sensing of Environment, 216: 201-211. - Campos-Taberner, M., García-Haro, F.J., Martínez, B., Sánchez-Ruiz, S. and Gilabert, M.A., 2019. A copernicus Sentinel-1 and Sentinel-2 classification framework for the 2020+ European common agricultural policy: A case study in València (Spain). Agronomy, 9(9): 556. - Češka, A., 1966. Estimation of the mean floristic similarity between and within sets of vegetational relevés. Folia Geobotanica et Phytotaxonomica, 1(2): 93-100. - Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of Environment, 37(1): 35-46. - Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstoun, E.B. and McMurtrey, J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229-239. - Delegido, J., Verrelst, J., Alonso, L. and Moreno, J., 2011. Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7): 7063-7081. - Dey, S., (n.d.). Radar vegetation index code for dual polarimetric Sentinel-1 data in EO browser. Available at: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-1/radar_vegetation_index_code_dual_polarimetric/supplementary_material.pdf - Eskandari, S. and Oladi, J., 2017. Modelling of forest fire spread using cellular automata. Geographical Planning of Space Quarterly Journal, 7(25): 38-54 (In Persian). - Fernandes, P.A.M., 2001. Fire spread prediction in shrub fuels in Portugal. Forest Ecology and Management, 144(1-3): 67-74. - Filgueiras, R., Mantovani, E.C., Althoff, D., Fernandes Filho, E.I. and Cunha, F.F.D., 2019. Crop NDVI monitoring based on Sentinel 1. Remote Sensing, 11(12): 1441. - Gazmeh, H., Alesheikh, A., Karimi, M. and Chehreghan, A., 2013a. Spatio-temporal forest fire spread modeling using cellular automata, Honey Bee Foraging and GIS. Bulletin of Environment, Pharmacology and Life Sciences, 3(1): 201-214. - Gazmeh, H., Chehreghan, A., Alesheikh, M.A. and Karimi, M., 2013b. Modelling forest fire spread using cellular automata, GIS and PSO. Geospatial Engineering Journal, 4(3): 71-86 (In Persian). - Ghaemi Rad, T. and Karimi, M., 2015. Evaluation performances of different forest fire spread models using cellular automata (case study: The forests of Lakan district in Rasht). Iranian Journal of Forest and Poplar Research, 23(1): 64-78 (In Persian). - Ghasemian Sorboni, N., Pahlavani, P. and Bigdeli, B., 2019. Vegetation mapping of Sentinel-1 and 2 satellite images using convolutional neural network and random forest with the aid of dual-polarized and optical vegetation indexes. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W18: 435-440. - Giannino, F., Russo, L., Ascoli, D., Migliozzi, A., Siettos, C.I. and Mazzoleni, S., 2017. Cellular automata simulation of forest fire behavior on Italian landscape: The case of Sardinia. AIP Conference Proceedings, 1906(1): 100006. - Grabska, E., Hostert, P., Pflugmacher, D. and Ostapowicz, K., 2019. Forest stand species mapping using the Sentinel-2 time series. Remote Sensing, 11(10): 1197. - Hernández Encinas, A., Hernández Encinas, L., Hoya White, S., Martín del Rey, A. and Rodríguez Sánchez, G., 2007. Simulation of forest fire fronts using cellular automata. Advances in Engineering Software, 38(6): 372-378. - Hernández Encinas, L., Hoya White, S., Martín del Rey, A. and Rodríguez Sánchez, G., 2007. Modelling forest fire spread using hexagonal cellular automata. Applied Mathematical Modelling, 31(6): 1213-1227. - Homchaudhuri, B., Kumar, M. and Cohen, K., 2013. Genetic algorithm based simulation-optimization for fighting wildfires. International Journal of Computational Methods, 10(6): 1350035. - Jahdi, R., Salis, M., Darvishsefat, A.A., Mostafavi, M.A., Alcasena, F., Etemad, V., ... and Spano, D., 2015. Calibration of FARSITE simulator in northern Iranian forests. Natural Hazards and Earth System Sciences, 15(3): 443-459. - Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization. Technical Report-tr06, Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey, 10p. - Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B. and Rakitin, V.Y., 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1): 135-141. - Norum, R.A. and Miller, M., 1984. Measuring fuel moisture content in Alaska: standard methods and procedures. General Technical Report PNW-GTR-171, U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and and Range Experiment Station, Portland, Oregon, 34p. - Periasamy, S., 2018. Significance of dual polarimetric synthetic aperture radar in biomass retrieval : An attempt on Sentinel-1. Remote Sensing of Environment, 217: 537-549. - Quartieri, J., Mastorakis, N.E., Iannone, G. and Guarnaccia, C., 2010. A cellular automata model for fire spreading prediction. Proceedings of the 3rd International Conference on Urban Planning and Transportation: Latest Trends on Urban Planning and Transportation. Corfù, Greece, 22-24 Jul. 2010: 173-179. - Rondeaux, G., Steven, M. and Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2): 95-107. - Rouse, Jr.J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Volume I: Technical presentations, Section A. Washington, D.C., 10-14 Dec. 1973: 309-317. - Rui, X., Hui, S., Yu, X., Zhang, G. and Wu, B., 2018. Forest fire spread simulation algorithm based on cellular automata. Natural Hazards, 91(1): 309-319. - Ryan, K.C., 2002. Dynamic interactions between forest structure and fire behavior in boreal ecosystems. Silva Fennica, 36(1): 13-39. - Scott, J.H. and Burgan, R.E., 2005. Standard fire behavior fuel models: A comprehensive set for use with Rothermel's surface fire spread model. General Technical Report RMRS-GTR-153, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, 72p. - Taylor, S.W., Woolford, D.G., Dean, C.B. and Martell, D.L., 2013. Wildfire prediction to inform fire management: Statistical science challenges. Statistical Science, 28(4): 586-615. - Velasquez, W., Munoz-Arcentales, A., Salvachua-Rodriguez, J. and Bohnert, T.M., 2019. Wildfire propagation simulation tool using cellular automata and GIS. Proceedings of International Symposium on Networks, Computers and Communications. Istanbul, Turkey, 18-20 Jun. 2019: 11p. - Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., Rüdiger, C. and Strauss, P., 2018. Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study. Remote Sensing, 10(9): 1396. | ||
|
آمار تعداد مشاهده مقاله: 904 تعداد دریافت فایل اصل مقاله: 589 |
||